The design roots of policy problems: Unpacking the role of procedural tools in design fitness and resilience
Bibliographic record
Abstract
Abstract While policy design scholars have made significant conceptual and empirical advances in identifying and evaluating procedural tools, there has been a little focus on understanding how they interact with the more traditional “substantive” elements of a policy mix and their critical functions in policy mix designs. As a result, there is uncertainty about how procedural tools affect policy effectiveness—at adoption or over time. To address this gap, we propose a framework for deconstructing policy mix designs to examine how procedural tools interact with substantive tools in ways that either contribute to or undermine design “fitness” and “resilience.” The framework's diagnostic utility is illustrated by its application to unpack healthcare arrangements in Singapore and India, which reveals design “fault lines” that policy researchers and practitioners need to be aware of. We conclude by offering research directions for further investigating the role of procedural tools in shaping policy dynamics and outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.091 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".